A Technology Review of Zero Knowledge Proof Techniques

Seyed Mohsen Rostamkolaei Motlagh

, Claus Pahl

1 a

, Hamid R. Barzegar

and Nabil El Ioini

2 b

Free University of Bozen-Bolzano, 39100 Bolzano, Italy

University of Nottingham, 43500 Semenyih, Malaysia

Keywords:

ZKP, Authentication, Distributed Systems, Technology Review.

Abstract:

Distributed systems, particularly in IoT, require robust privacy-preserving authentication mechanisms to ad-

dress increasing concerns about data security and integrity. Zero-Knowledge Proofs (ZKPs) have emerged

as a promising solution to balance security, privacy, and efﬁciency. This paper reviews and compares state-

of-the-art ZKP protocols, focusing on their suitability for decentralized, resource-constrained environments.

We propose a comprehensive evaluation framework and apply it to zk-SNARK, zk-STARK, and Bulletproof

protocols, analyzing metrics such as scalability, efﬁciency, and proof size. Our ﬁndings provide actionable

insights into the trade-offs between these protocols, offering guidance for their application in IoT systems.

1 INTRODUCTION

Zero-knowledge proofs (ZKPs) are a cryptographic

tool that can enhance the security of IoT devices.

ZKPs enable one party (the prover) to prove to an-

other party (the veriﬁer) that a given statement is true

without revealing any additional information beyond

the truth of the statement itself. This characteris-

tic is particularly valuable in distributed applications,

where it is often necessary to verify identities and

transactions while preserving the privacy of the un-

derlying data. Traditional authentication mechanisms

often require sharing or exposing some level of infor-

mation that could potentially be intercepted or mis-

used. ZKPs mitigate this risk by proving the validity

of a claim without disclosing any additional data.

Scalability Issues: distributed systems can have

larger numbers of nodes and devices, each requiring

authentication. Centralized systems may struggle to

handle the high volume of authentication requests,

leading to latency issues (Yang et al., 2017).

Single Point of Failure: Centralized authentication

systems create a single point of failure (Roman et al.,

2013).

Intermittent Connectivity: Many devices and

nodes, e.g., in mobility scenarios, may experience in-

termittent connectivity (Atzori et al., 2010).

Resource Constraints: Many devices and nodes

have limited resources. Traditional authentication

may require extensive cryptographic operations and

https://orcid.org/0000-0002-9049-212X

https://orcid.org/0000-0002-1288-1082

can be resource-intensive (Mukherjee et al., 2017).

To address these challenges, ZKPs offer secure

authentication without revealing any sensitive in-

formation, facilitating the following in distributed,

decentralised contexts:

Scalable Authentication: ZKPs support decen-

tralized authentication mechanisms, reducing the

reliance on central servers and enhancing scalability.

Resilience to Connectivity Issues: ZKPs can enable

authentication protocols that do not require constant

connectivity to a central server, making them suitable

for mobile and intermittently connected devices.

Efﬁciency for Resource-Constrained Devices:

Certain ZKP protocols are designed to be computa-

tionally efﬁcient, making them suitable for devices

with limited resources.

Enhanced Privacy: ZKPs do not disclose sensitive

information during the authentication, addressing

privacy concerns in open environments (Goldwasser

et al., 2019).

We evaluate different ZKP techniques to deter-

mine their suitability for the above challenges, con-

sidering factors such as efﬁciency, scalability, and se-

curity (Werth et al., 2023a; Werth et al., 2023b).

We evaluate different ZKP techniques based on a

comprehensive assessment framework to cover vari-

ous ZKP protocols. The evaluation will consider fac-

tors such as efﬁciency, scalability, and security.

Rostamkolaei Motlagh, S. M., Pahl, C., Barzegar, H. R. and El Ioini, N.

A Technology Review of Zero Knowledge Proof Techniques.

DOI: 10.5220/0013269600003944

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 10th International Conference on Internet of Things, Big Data and Security (IoTBDS 2025), pages 245-253

ISBN: 978-989-758-750-4; ISSN: 2184-4976

245

2 ZKP TECHNOLOGY

BACKGROUND

A trusted setup refers to a phase in initialising a

cryptographic protocol where certain parameters (of-

ten random values) are generated. These parame-

ters are crucial for the security of the system. The

key characteristic of a trusted setup is that the secu-

rity of the entire system depends on the secrecy and

proper disposal of initial data used during this setup

phase (El Ioini and Pahl, 2018). In protocols that

use a trusted setup, such as zk-SNARKs, the setup

phase involves generating a set of public parameters

that are used to construct and verify zero-knowledge

proofs. Parameter Generation: During the trusted

setup, The generation of initial cryptographic material

might happen. This is used to derive public param-

eters for the ZKP system. Public and Private Data:

Public parameters can be safely shared and are nec-

essary for users to generate and verify proofs. Pri-

vate data, if leaked, could be used to forge proofs,

and must be destroyed or kept secret. Proof Gener-

ation and Veriﬁcation: Once the parameters are set

up, users can generate proofs that demonstrate their

knowledge of certain information without revealing

the information itself. Veriﬁers use the public param-

eters to check proof validity. The main security con-

cern with a trusted setup is the trust assumption it-

self. Participants must trust that the setup ceremony

was conducted honestly and that all copies of the toxic

waste were destroyed. A reliance on a few during the

setup creates a central point of failure. Corruption or

coercion of participants in the trusted setup phase can

lead to security failures.

ZKPs can be classiﬁed into interactive and

non-interactive. Interactive zero-knowledge proofs

(IZKPs) involve a series of back-and-forth commu-

nications where the veriﬁer sends challenges to the

prover, who responds with answers that demonstrate

knowledge of a secret, without revealing the se-

cret itself. Non-interactive zero-knowledge proofs

(NIZKPs) require only a single message from the

prover to the veriﬁer if interaction is not feasible.

2.1 Interactive ZKPs

Fiat-Shamir Protocol: Conceptualized as an interac-

tive proof system, the protocol involved a dynamic ex-

change between the prover and the veriﬁer where the

veriﬁer would send random challenges to the prover,

who, in turn, would respond in a way that convinc-

ingly demonstrated their knowledge of a secret with-

out revealing the secret itself. Fiat and Shamir de-

vised an adaptation known as the Fiat-Shamir heuris-

tic. This non-interactive version used a cryptographic

hash function to simulate the veriﬁer’s random chal-

lenges. It is used for digital signatures and secure au-

thentication systems (Fiat and Shamir, 1986).

Schnorr Protocol: builds on proving the posses-

sion of a discrete logarithm, being effective in digital

signatures and identity veriﬁcation. The protocol op-

erates in an interactive setting where the veriﬁer sends

a random challenge to the prover. The prover must

demonstrate knowledge of a secret discrete logarithm

in response to this challenge (Schnorr, 1990).

Guillou-Quisquater Protocol: is an interactive

ZKP system designed for RSA-like cryptographic set-

tings. This protocol is used to prove knowledge of

k-th roots modulo a composite number. Speciﬁcally,

it allows a prover to demonstrate that they possess a

secret value. It serves as a foundation for RSA-based

interactive identiﬁcation schemes, providing a secure

method for identity veriﬁcation by ensuring that the

veriﬁer can be convinced of the prover’s knowledge

without gaining any additional information about the

secret (Guillou and Quisquater, 1988).

Feige-Fiat-Shamir Protocol: Building upon the

Fiat-Shamir protocol, this improves security by uti-

lizing multiple secret values, thus strengthening au-

thentication. It allows a prover to demonstrate their

identity without revealing their secrets. This approach

provides a more secure and robust method for identity

veriﬁcation, using principles of ZKP to ensure that the

veriﬁer is convinced of the prover’s identity without

any additional information (Fiege et al., 1987).

Graph Isomorphism Protocol: can demonstrate

that two graphs are isomorphic without revealing the

isomorphism itself. This protocol involves multiple

rounds in which the veriﬁer challenges the prover to

demonstrate the isomorphism of randomly permuted

graphs. The veriﬁer sends a randomly permuted

version of one graph, and the prover must respond

by showing the isomorphism between permuted and

original graphs (Goldwasser et al., 2019).

2.2 Non-Interactive ZKPs

zk-SNARKs (Zero-Knowledge Succinct Non-

Interactive Arguments of Knowledge): represent a

cryptographic tool that allows a prover to demon-

strate possession of certain information without the

need for interactive veriﬁcation. One of the key

features of zk-SNARKs is conciseness, i.e., proofs

are both small in size and quick to verify. This

makes zk-SNARKs well-suited for applications in

blockchain technologies (Ben-Sasson et al., 2014;

Pahl and El Ioini, 2019; Berenjestanaki et al., 2023).

zk-STARKs (Zero-Knowledge Scalable Transpar-

IoTBDS 2025 - 10th International Conference on Internet of Things, Big Data and Security

246

ent Arguments of Knowledge): are similar to zk-

SNARKs with some differences. One of the main

features of zk-STARKs is their transparency, i.e., not

requiring a trusted setup. Additionally, zk-STARKs

are designed to be post-quantum secure, making them

resistant to potential future attacks by quantum com-

puters. These characteristics make zk-STARKs an

emerging technology in the blockchain space, where

they are used to enhance scalability and security with-

out compromising transparency.

Bulletproofs: are a type of non-interactive zero-

knowledge proof that stand out for their compactness

and efﬁciency. Unlike many other zero-knowledge

proofs, Bulletproofs do not require a trusted setup

phase, making them more practical and secure. They

are particularly noted for their effectiveness in range

proofs, which are essential in verifying that a secret

value lies within a certain range without revealing the

value itself. This feature is crucial in the context of

cryptocurrencies citebunz2018bulletproofs.

Groth-Sahai Proofs: provide an efﬁcient way to

construct non-interactive zero-knowledge proofs for

statements involving bilinear maps. These proofs are

particularly valuable in cryptographic protocols that

require the veriﬁcation of complex relationships be-

tween elements, such as ciphertexts in attribute-based

encryption schemes. By leveraging the properties of

bilinear maps, Groth-Sahai proofs enable the secure

and efﬁcient veriﬁcation of cryptographic operations

(Groth and Sahai, 2008).

ZKBoo/ZKB++: is a framework designed to con-

struct non-interactive zero-knowledge proofs that are

both efﬁcient and scalable. It achieves this by allow-

ing the veriﬁcation of computations without reveal-

ing the underlying data or software. ZKB++ builds

upon the ZKBoo framework, introducing optimiza-

tions that reduce proof size and computational over-

head. This makes ZKB++ particularly suitable for ap-

plications that require the secure outsourcing of com-

putations (Giacomelli et al., 2016).

Ligero: is a lightweight zero-knowledge proof

protocol designed to be both scalable and efﬁcient,

reducing the computational and communication over-

head compared to other protocols like SNARKs or

STARKs. Its design focuses on minimizing proof size

and veriﬁcation complexity, making it a strong candi-

date for applications requiring efﬁcient cryptographic

proofs citeames2017ligero.

PLONK: (Permutations over Lagrange-bases for

Oecumenical Noninteractive arguments of Knowl-

edge) is a SNARK that simpliﬁes proof creation and

veriﬁcation using a single reference string for any

computation. This eliminates the need for multiple

trusted setups for different computations, enhancing

versatility and efﬁciency. PLONK can improve scala-

bility and privacy (Gabizon et al., 2019).

2.3 Other Variants

One advancement is auxiliary-input zero-knowledge

(Goldreich and Oren, 1994). This form of zero-

knowledge proof addresses scenarios where the ver-

iﬁer may possess prior knowledge related to the as-

sertion. The deﬁnition ensures that the proof pro-

tocol safeguards against any additional information

leakage, even if the veriﬁer starts with auxiliary in-

formation. Another advancement is the blackbox-

simulation zero-knowledge, which further reﬁnes the

security model of zero-knowledge proofs. In this ap-

proach, the proof’s security is tested by simulating the

interaction between the prover and the veriﬁer using a

’black box’ method, ensuring the veriﬁer cannot dis-

tinguish between the simulated interaction and the ac-

tual proof process. Boyar, Friedl, and Lund (Boyar

et al., 1991) introduce new techniques for construct-

ing ZKPs that are not only efﬁcient but also reduce

the computational demands on the prover. Their work

addresses a key challenge in the practical implemen-

tation of zero-knowledge proofs by reducing the com-

plexity and power requirements, making these proofs

accessible for everyday cryptographic applications.

2.4 Integrating Blockchain and ZKPs

To address distributed systems challenges, integrating

blockchain and zero-knowledge proofs (ZKP) into ac-

cess control systems has been proposed. Blockchain

technology offers a decentralized framework that en-

hances data integrity and security by distributing the

control and veriﬁcation of transactions across mul-

tiple nodes. This eliminates the need for a central

authority and makes the system more resilient to at-

tacks. Every transaction or access request is logged

on an immutable distributed ledger, ensuring trans-

parency and reducing data tampering (Song et al.,

2021).(Alkhamisi and Alboraei, 2019) further support

this by illustrating how decentralized systems can al-

leviate the credibility problem posed by third-party

information concentration.

ZKPs allow a party to prove they have certain

knowledge without revealing the knowledge itself.

This method is particularly beneﬁcial for maintaining

privacy in access control systems. For instance, Song

et al. (2021) proposed a model where access per-

missions are managed using encrypted tokens based

on ZKP. This ensures that user identities remain hid-

den, and the attributes required for access are not ex-

posed, thereby enhancing privacy. Jedlicka and Grant

A Technology Review of Zero Knowledge Proof Techniques

247

(2022) also highlight the potential of ZKP in main-

taining data privacy, noting that it allows veriﬁcation

without data exposure (Jedlicka and Grant, 2022).

Smart contracts, which are self-executing con-

tracts with the terms of the agreement directly writ-

ten into code, play a vital role in improving the ef-

ﬁciency of IoT access control systems. By imple-

menting access control policies as smart contracts on

the blockchain, these systems can automate policy

enforcement, thus reducing the computational bur-

den on IoT devices and optimizing resource usage

(Song et al., 2021). (Lin et al., 2023) add that smart

contracts can be particularly effective in handling

high-trafﬁc environments by batching authorization

requests, thereby improving overall system efﬁciency.

3 COMPARATIVE ZKP

ANALYSIS

We ﬁrst deﬁne the comparison criteria, then describe

the selection of protocols and the comparison process,

before presenting the results of the comparison.

3.1 ZKP Comparison Criteria

The selection of ZKP techniques was guided by sev-

eral key criteria to ensure their suitability for dis-

tributed systems environments in general and con-

strained, decentralised ones in particular. We intro-

duce criteria together with suitable metrics.

Scalability: The ability of the ZKP technique to man-

age a large number of devices and high volumes of

data efﬁciently. Scalability is important in where a

large number of devices or nodes may be connected.

In practice, scalability is measured by evaluating the

proof generation and veriﬁcation times as the number

of devices and data size increase. We also assess the

system’s ability to maintain performance under differ-

ent network conditions and loads.

Efﬁciency: The computational and communication

efﬁciency of ZKP, especially in terms of proof gen-

eration and veriﬁcation times. This is important for

real-time applications and for devices with limited

processing power and energy resources. Efﬁciency

is measured by the time taken to generate and verify

proofs, the computational resources are required, as

well as the communication overhead associated with

sending proofs. Lower values show higher efﬁciency.

Security Robustness: The robustness of ZKP against

various types of attacks, including quantum attacks, is

necessary to maintain integrity and conﬁdentiality of

the data. Ensuring the security of the proofs is impor-

tant to protect against unauthorized access and tam-

pering. Security is assessed based on the theoretical

guarantees provided by the ZKP technique, such as

resistance to known attack vectors.

No Trusted Setup: Techniques that do not require a

trusted setup phase are preferred to eliminate the need

for a trusted third party and reduce the risk of com-

promised security. Setup requirements are evaluated

based on the complexity, duration, and necessity of a

trusted setup phase. Techniques without trusted setup

requirements are considered more favourable.

Compact Proof Size: Smaller proof sizes are advan-

tageous for IoT devices with limited storage capabili-

ties and for reducing the communication overhead in

constrained network environments. Proof size is mea-

sured in bytes. Smaller proof sizes are preferable as

they minimize storage and bandwidth needs.

Privacy-Preserving Capabilities: The ability of

ZKP to maintain privacy by not revealing any in-

formation beyond the validity of the statement being

proved. Privacy-preserving capabilities are assessed

based on the theoretical foundations of the ZKP tech-

nique and practical evaluations of the information

leakage during proof generation and veriﬁcation.

Applicability to Mobility Scenarios: Assessing how

well the ZKP technique can be integrated into mo-

bile scenarios, such as connected vehicles and wear-

able health monitors. Mobility scenarios introduce

additional challenges such as dynamic network en-

vironments and frequent handovers. Techniques that

perform well in these scenarios ensure reliable and

secure operation. Applicability is evaluated based

on the performance of the ZKP technique in simu-

lated mobility scenarios, considering factors such as

proof generation and veriﬁcation times, communica-

tion overhead, and resilience to network changes.

3.2 Selection and Comparison Process

The process of selecting the ZKP techniques involved

a comprehensive review of existing ZKP protocols

and evaluating them against the aforementioned cri-

teria. The selection process included the following:

Literature Review: An extensive review of the

literature on various ZKP techniques was conducted

to identify potential candidates. This included

reviewing academic papers, technical reports, and

industry publications.

Evaluation of Features: The identiﬁed ZKP tech-

niques were evaluated based on their features, such

as proof generation and veriﬁcation times, security

properties, and whether they required a trusted setup.

Comparison and Analysis: The techniques were

compared against each other based on the selection

criteria. Techniques that best met the criteria were

IoTBDS 2025 - 10th International Conference on Internet of Things, Big Data and Security

248

shortlisted for evaluation.

Two tables provide a comparative analysis of sev-

eral ZKP techniques, highlighting their key features

and suitability for IoT applications. Table 1 focuses

on technical aspects, including type of ZKP, com-

munication cost, proof size, and setup requirements

(Ben-Sasson et al., 2018). These attributes determine

efﬁciency and practicality of each ZKP technique in

applications where bandwidth and storage are limited.

Table 2 details potential applications, advantages, and

disadvantages of each ZKP technique (B

unz et al.,

2018; Sun et al., 2021; Ames et al., 2017; Gabizon

et al., 2019; Groth and Sahai, 2008). This information

provides a comprehensive overview of the suitability

of each technique for speciﬁc IoT use cases and high-

lights the trade-offs involved in using each method.

3.3 Selected ZKP Protocol Analysis

We now detail three signiﬁcant ZKP protocols: zk-

SNARKs, zk-STARKs, and Bulletproofs. These pro-

tocols have substantial applications in areas such as

blockchain technology, privacy-preserving computa-

tions, and secure communications. We explore each

protocol covering the following: Working Mecha-

nism: in-depth explanation of how the protocol oper-

ates, detailing the main phases involved. Proof Gen-

eration and Veriﬁcation Times: discussion on the ef-

ﬁciency of the protocol in terms of proof generation

and veriﬁcation times. Comparative Metrics: com-

parison highlighting strengths and trade-offs of the

protocol in terms of proof size, generation, and ver-

iﬁcation times. Advantages and Disadvantages: anal-

ysis of the protocol’s main beneﬁts and limitations.

3.3.1 ZK-SNARKs

zk-SNARKs allow a prover to convince a veriﬁer that

a particular statement is true without revealing any

additional information, thereby maintaining the con-

ﬁdentiality of the data involved.

Working Mechanism of zk-SNARKs: In the setup

phase, a trusted party generates a Common Reference

String (CRS), a set of cryptographic parameters es-

sential for both the proving and veriﬁcation processes.

This CRS is critical as it ensures the integrity and se-

curity of the subsequent proofs. During the proving

phase, the prover uses the CRS to create a proof that a

given statement is true. This process involves encod-

ing the statement and the computation that veriﬁes it

into a succinct proof. The challenge here is to trans-

form potentially large and complex data into a com-

pressed form that retains its validity and can be efﬁ-

ciently veriﬁed. In the veriﬁcation phase, the veriﬁer

uses the CRS and the proof to check the validity of the

statement. This phase is designed to be extremely efﬁ-

cient, enabling quick veriﬁcation of the proof without

requiring the veriﬁer to repeat the original computa-

tion. This efﬁciency is crucial for applications requir-

ing real-time veriﬁcation, such as blockchain transac-

tions.

Proof Generation and Veriﬁcation Times: The time

required to generate a zk-SNARK proof can vary

based on several factors, including the complexity of

the computation, the optimization of the implementa-

tion, and the hardware used. More complex computa-

tions naturally require more time to generate proofs.

Comparative Metrics: When considering the proof

generation time, veriﬁcation time, and proof size of

these implementations, the differences highlight the

strengths and trade-offs of each approach.

3.3.2 ZK-STARK

zk-STARKs offer an advancement by eliminating the

need for a trusted setup and providing a proof system

that scales efﬁciently with large data sets.

Working Mechanism of zk-STARKs: The zk-

STARK protocol consists of several key phases:

setup, proving, and veriﬁcation. These phases are

designed to ensure that the system is both scalable

and secure, without the need for any initial trusted

setup. 1) Transparent Setup. Unlike zk-SNARKs,

zk-STARKs do not require a trusted setup. Instead,

they utilize publicly veriﬁable randomness, ensuring

transparency and eliminating the risks associated with

a trusted setup. This is achieved through the use of

cryptographic primitives that are publicly known and

veriﬁable. 2) Interactive Oracle Proofs (IOPs). zk-

STARKs leverage IOPs to achieve scalability and efﬁ-

ciency. IOPs allow the prover to interact with the veri-

ﬁer through a series of queries to an oracle, which pro-

vides the necessary information to validate the proof.

This interaction can be structured to ensure that the

proof remains succinct and easy to verify. 3) Low-

Degree Testing and Error-Correcting Codes. The pro-

tocol employs low-degree testing and error-correcting

codes to ensure that the proofs are both correct and re-

sistant to errors. These techniques allow zk-STARKs

to maintain integrity even in the presence of noisy

data or computational errors.

Steps in zk-STARK Protocol: The setup generates

public parameters using publicly veriﬁable random-

ness. During proving, the prover constructs a proof by

encoding computation and data for it to be efﬁciently

veriﬁed in several rounds of interaction with the veri-

ﬁer, during which the prover responds to queries from

the veriﬁer’s oracle. Veriﬁcation involves the veriﬁer

checking the proof using the information provided by

A Technology Review of Zero Knowledge Proof Techniques

249

Table 1: Comparative Analysis of ZKP Techniques – Part 1 Technical Focus.

ZKP Type Communication

Cost

Proof Size Setup Requirements

Groth’s zkSNARK Non-interactive Low Moderate Requires a trusted setup

PLONK Non-interactive Low Very Small Does not require a trusted setup

FRI Non-interactive Very Low Medium Does not require a trusted setup

ZKBoo Non-interactive Very Low Very Small Does not require a trusted setup

Halo General framework Varies Varies Varies

Bulletproofs Non-interactive Low Very Small Does not require a trusted setup

zk-STARKs Non-interactive Medium Large Does not require a trusted setup

zk-SNARK Non-interactive Low Moderate Requires a trusted setup

Ligero Non-interactive Medium Medium Does not require a trusted setup

Table 2: Comparative Analysis of ZKP Techniques – Part 2 Applicability Focus.

ZKP Potential Applications Advantages Disadvantages

Groth’s zk-

SNARK

Privacy-preserving cryptocurrencies,

anonymous credentials

Efﬁcient, proofs are

relatively small

requires trusted setup, vulnerable

to attacks if setup compromised

PLONK Privacy-preserving cryptocurrencies, se-

cure voting systems

Very efﬁcient, proofs

are very small

Can be less versatile

FRI Privacy-preserving applications Very efﬁcient can

handle complex com-

putations

Can be difﬁcult to implement

ZKBoo Privacy-preserving applications Very efﬁcient, proofs

are very small

Can be less versatile

Halo Privacy-preserving cryptocurrencies,

anonymous credentials

Flexible, versatile Complex, requires expertise to

implement

Bulletproofs Conﬁdential transactions, privacy-

preserving applications

No trusted setup,

compact proofs

Higher computational cost for

proof generation

zk-

STARKs

Large-scale computations, blockchain Scalable, post-

quantum secure

Larger proof sizes

zk-SNARK Privacy-preserving cryptocurrencies,

anonymous authent., secure voting

Efﬁcient, proofs are

relatively small

Requires a trusted setup, can be

vulnerable to trust attacks

Ligero Privacy-preserving applications Efﬁcient, medium-

sized proofs

Can be less efﬁcient in some use

cases

the prover and the publicly known parameters. This

is designed to be efﬁcient, allowing for rapid veriﬁca-

tion of large computations.

Comparative Metrics: When comparing zk-

STARKs to other cryptographic proof systems, sev-

eral key metrics highlight their advantages: Proof

Generation Time: zk-STARKs are designed to gen-

erate proofs efﬁciently, even for large data sets. The

time required for proof generation scales logarithmi-

cally with the size of the data, making it feasible

for real-world applications involving large amounts

of data. Veriﬁcation Time: The veriﬁcation process

is highly optimized, allowing for rapid veriﬁcation of

proofs. This efﬁciency is crucial for applications re-

quiring real-time validation, such as blockchain trans-

actions and large-scale data analysis. Proof Size: zk-

STARKs produce much smaller proofs than the data

they represent by using advanced cryptographic tech-

niques, making proofs easy to store and transmit.

Advantages of zk-STARKs: Scalability: zk-

STARKs are designed to handle large-scale data ef-

ﬁciently. The proof generation and veriﬁcation pro-

cesses are optimized to scale logarithmically with

the size of the data, making zk-STARKs suitable

for applications involving massive data sets. Trans-

parency: By eliminating the need for a trusted setup,

zk-STARKs enhance transparency and trustworthi-

ness. The use of publicly veriﬁable randomness en-

sures that all parties can trust the setup process with-

out relying on a single entity. Post-Quantum Security:

zk-STARKs are designed to be secure against quan-

tum computing attacks. This post-quantum security

is achieved through the use of cryptographic primi-

tives that are resistant to the capabilities of quantum

computers. Efﬁciency: The efﬁciency of proof gen-

eration and veriﬁcation makes zk-STARKs practical

for real-world applications. The compact proofs and

rapid veriﬁcation are crucial for real-time validation.

3.3.3 Bulletproofs

Bulletproofs are a cryptographic protocol designed

to facilitate range proofs, a mechanism that veriﬁes

whether a hidden numerical value falls within a pre-

IoTBDS 2025 - 10th International Conference on Internet of Things, Big Data and Security

250

determined interval without revealing the value itself.

Introduced to enhance privacy and security, Bullet-

proofs are particularly important in ﬁnancial systems

and secure credential veriﬁcation.

Working Mechanism of Bulletproofs: Bulletproofs

operate without the need for a trusted setup phase,

which contrasts with other ZKP systems like zk-

SNARKs, using the Fiat-Shamir heuristic to achieve

non-interactive zero-knowledge proofs.

Proof Construction: The prover generates vectors

and scalars that encode the binary representation of

the secret value x. A series of commitments and poly-

nomials obfuscate the actual value while proving its

legitimacy within the claimed range. This involves

commitments to auxiliary vectors that mask the orig-

inal input’s structure.

Proof Generation: The prover uses Pedersen com-

mitments, a cryptographic commitment scheme, to

commit to the secret value x without revealing it. The

commitment C is typically calculated as C = g

where g and h are public generator points of a cyclic

group, x is the secret value, and r is a random nonce.

Veriﬁcation: The veriﬁer checks these commitments

against the public parameters to conﬁrm the proof’s

validity. This involves ensuring that the inner prod-

uct argument holds, proving that the committed value

lies.

Proof Generation and Veriﬁcation Times: Bullet-

proofs offer signiﬁcant efﬁciency in proof generation

and veriﬁcation:

Proof Generation Time: The time to generate Bul-

letproofs scales logarithmically with the number of

bits of the value. This efﬁciency is particularly beneﬁ-

cial in environments requiring rapid proof generation.

Veriﬁcation Time: Although individual veriﬁca-

tion can be computationally intensive compared to

some alternatives, batch veriﬁcation is highly efﬁ-

cient, making Bulletproofs practical for systems pro-

cessing large transaction volumes.

Proof Size: The proof sizes in Bulletproofs are signif-

icantly smaller, scaling logarithmically with the wit-

ness size, which reduces storage and bandwidth re-

quirements.

Comparative Metrics: When compared to other

cryptographic proof systems, Bulletproofs demon-

strate the following advantages and trade-offs. Efﬁ-

ciency: Bulletproofs do not require a trusted setup and

offer compact proofs, making them efﬁcient for stor-

age and transmission. Veriﬁcation Complexity: While

batch veriﬁcation is efﬁcient, individual veriﬁcation

can be more computationally intensive than protocols

like zk-SNARKs. Scalability: Bulletproofs scale log-

arithmically with the witness size, making them suit-

able for large-scale applications, although the compu-

tational burden on the prover can be signiﬁcant.

Advantages: (i) No trusted setup required, reducing

the risk of malicious setup. (ii) Smaller proof sizes,

enhancing storage and transmission efﬁciency. (iii)

Efﬁcient batch veriﬁcation, beneﬁcial for processing

large volumes of transactions.

Disadvantages: (i) Individual veriﬁcation is more

complex and computationally intensive. (ii) Signiﬁ-

cant computational burden on the prover, especially

in aggregated proofs or MPC settings. (iii) Potential

scalability challenges due to computational complex-

ities.

4 LIMITATIONS AND

CHALLENGES

While the integration of blockchain and ZKP)in ac-

cess control systems is promising, limitations and

challenges remain for effective and widespread adop-

tion in challenging contexts and in combination with

blockchains.

Scalability and Performance: One of the primary

challenges is the scalability of blockchain systems.

Blockchain’s decentralized nature, while beneﬁcial

for security and transparency, often results in high la-

tency and low transactions per second (TPS). This is

particularly problematic in high-trafﬁc network envi-

ronments where rapid and frequent transactions are

required. Blockchain-based access control systems

struggle with scalability due to low TPS and high la-

tency (Lin et al., 2023).

Computational Overhead: The computational de-

mands of zero-knowledge proofs can be a challenge

for devices with limited processing power and energy

supply. Implementing ZKP in resource-constrained

environments requires efﬁcient algorithms to min-

imize computational overhead. Lightweight ZKP

implementations can ensure that devices can per-

form necessary computations without excessive en-

ergy consumption (Song et al., 2021).

Privacy and Data Integrity: Ensuring privacy and

data integrity in a decentralized setting is complex.

Although blockchain technology provides an im-

mutable ledger, the public nature of blockchain can

lead to potential privacy issues. ZKP can help main-

tain user privacy by allowing veriﬁcation without data

exposure (Jedlicka and Grant, 2022).

Integration with Existing Systems: Integrating

blockchain and ZKP technologies with existing IoT

systems is another challenge. Many current IoT de-

vices and infrastructures are not designed to support

the high computational and storage requirements of

blockchain and ZKP (Alkhamisi and Alboraei, 2019).

A Technology Review of Zero Knowledge Proof Techniques

251

Security Concerns: While blockchain and ZKP en-

hance security, they are not immune to all attacks. Po-

tential vulnerabilities in cryptographic algorithms can

be exploited, and ensuring robust security protocols is

essential (Sun et al., 2021).

Cost of Implementation: The cost associated with

implementing blockchain and ZKP technologies can

be prohibitive. This includesﬁnancial costs but also

time and resources required for development, deploy-

ment, and maintenance (Derei et al., 2023).

5 CONCLUSIONS

We explored the foundational concepts and develop-

ments in zero-knowledge proofs (ZKPs), a crypto-

graphic technique that ensures the validity of a state-

ment without revealing any additional information.

We provided 1. Introduction to Zero-Knowledge

Proofs as an overview of ZKPs, including their ori-

gin and fundamental principles. 2. Types of Zero-

Knowledge Proofs providing a differentiation exam-

ination of the different types of ZKPs, including in-

teractive and non-interactive proofs. 3. Classiﬁca-

tion of Common Protocols based on Deﬁned Assess-

ment criteria - following a systematic selection pro-

cess. 4. Advanced Protocols like zkSNARKs, zk-

STARKs, Bulletproofs, comparing their mechanisms,

advantages, and limitations.

Our review shows that decentralisation or resource

limitations create challenges such as scalability, com-

putational overhead, privacy and data integrity, wider

security concerns and cost to be addressed.

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